Hope everyone had a wonderful Labor Day weekend. During my time off CartelCapers, I have been working on several research projects. In this post, I’d like to give the interested readers an update on two of them.

When Machines Learn to Collude: Lessons from a Recent Research Study on Artificial Intelligence

From Professors Maurice Stucke and Ariel Ezrachi’s Virtual Competition published a year ago, to speeches by the Federal Trade Commission Commissioner Terrell McSweeny and Acting Chair Maureen K. Ohlhausen, to an entire issue of a recent CPI Antitrust Chronicles, and a conference hosted by Organisation for Economic Co-operation and Development (OECD) in June this year, there has been an active and ongoing discussion in the antitrust community about computer algorithms. In a short commentary (downloadable here), I briefly summarize the current views and concerns in the antitrust and artificial intelligence (AAI) literature pertaining to algorithmic collusion and then discuss the insights and lessons we could learn from a recent AI research study. As I argue in this article, not all assumptions in the current antitrust scholarship on this topic have empirical support at this point.

Sub-regressions, F test, and Class Certification

Did the anticompetitive conduct impact all or nearly all class members? This question is central to a court’s class certification decision. And to answer the question, a methodology—known as sub-regressions (also labelled less informatively as simply the “F test” in the recent Drywall litigation)—is being increasingly employed, particularly by defendants’ expert witnesses. A key step of a sub-regression type analysis is to partition the data into various sub-groups and then to examine data poolability.[1]

Forthcoming in the Journal of Competition Law & Economics, my article titled “To Pool or Not to Pool: A Closer Look at the Use of Sub-Regressions in Antitrust Class Certification” focuses on three areas of interest pertaining to sub-regressions:

Several methodological challenges, many of which have not been previously acknowledged, as well as potential ways to address them. Speciﬁcally, what test should one use? How does one choose the subsets or partitions of data to test? Are individual estimates of damages always the most reliable approach when we believe the impact varies across customers or across some other dimensions?

This paper is currently being processed at the Journal. If you would like a copy, please feel free to reach out to me.

As always, I appreciate your thoughts and comments. You can reach me at ai.deng@bateswhite.com or connect with me on LinkedIn [here].

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The US Supreme Court has called cartels "the supreme evil of antitrust." Price fixing and bid rigging may not be all that evil as far as supreme evils go, but an individual can get 10 years in jail and corporations can be fined hundreds of millions of dollars. This blog will provide news, insight and analysis of the world of cartels based on the many years my colleagues and I have as former feds with the Antitrust Division, USDOJ.